---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- art
- movies
pretty_name: IMDB Reviews (Aspect based Formatted)
size_categories:
- 100K<n<1M
---

# IMDB Reviews (Aspect Based Formatted)

## Overview

**IMDB Reviews (Aspect Based Formatted)** is a specialized dataset designed for text classification tasks that involve identifying and categorizing specific aspects of movie reviews. The dataset focuses on extracting and labeling various elements of filmmaking, such as cinematography, story, characters, direction, and unique concepts, from user reviews on IMDB.

The dataset can be used to develop and train models for aspect-based sentiment analysis, providing insights into how different aspects of a movie are perceived by audiences. This structured approach allows for more granular sentiment analysis and understanding of reviews, enhancing applications like movie recommendation systems, sentiment analysis tools, and more.

## License

This dataset is released under the Apache 2.0 License, allowing for both commercial and non-commercial use. For full details, please refer to the [license file](LICENSE).

## Task Categories

- **Text Classification**: The primary task for this dataset is classifying text into predefined categories, specifically focusing on various aspects of movie reviews.
- **Aspect-Based Sentiment Analysis**: Another critical task is analyzing sentiments related to specific aspects of filmmaking to understand audience perceptions better.

## Language

- **English (en)**: The dataset consists of reviews written in English, making it suitable for English language text processing and analysis tasks.

## Tags

- **Art**: The dataset provides insights into artistic elements of films, such as cinematography and direction.
- **Movies**: Focuses on movie reviews, analyzing various aspects of filmmaking and audience sentiment.

## Pretty Name

**IMDB Reviews (Aspect Based Formatted)**

## Size Categories

- **100K < n < 1M**: The dataset contains a substantial number of entries, making it suitable for training and evaluation of machine learning models that require a significant amount of data.

## Dataset Structure

The dataset is structured to provide comprehensive information about movie reviews and the aspects they focus on. Here's a breakdown of the main columns:

- **Review**: The complete text of the movie review, providing context and content for analysis.
- **Aspect**: The specific aspect of filmmaking that the review addresses, such as Story, Characters, or Unique Concept.
- **Snippets**: Key phrases or sentences extracted from the review that relate specifically to the identified aspect.
- **Aspect Encoded**: A numerical encoding representing the aspect for easy use in machine learning models.

### Example Entry

| Review ID | Review Snippet                                                                 | Aspect         | Snippets                                      | Aspect Encoded |
|-----------|--------------------------------------------------------------------------------|----------------|-----------------------------------------------|----------------|
| 0         | The cinematography was stunning, but the story was weak.                        | Story          | [The story was weak]                          | 5              |
| 1         | I loved the movie. There wasn't anything unique in the movie.                   | Unique Concept | [There wasn't anything unique in the movie]   | 6              |
| 2         | Characters could've been better tho.                                            | Characters     | [Characters could've been better tho]         | 0              |

## Dataset Features

1. **Aspect Diversity**: Includes a wide range of aspects related to filmmaking, providing a holistic view of movie reviews.
2. **Aspect-Specific Sentiments**: Enables analysis of sentiments specific to each aspect, improving understanding of audience perceptions.
3. **Structured Format**: Organized in a structured format that facilitates easy integration with text classification models.

## Use Cases

- **Aspect-Based Sentiment Analysis**: Train models to analyze sentiments related to specific movie aspects.
- **Recommendation Systems**: Enhance movie recommendation algorithms by considering detailed opinions on different aspects.
- **Market Research**: Understand audience preferences and perceptions to inform filmmaking and marketing strategies.

## Contribution

We welcome contributions to this dataset in the form of additional reviews, aspect annotations, or improvements to existing entries. Please refer to our contribution guidelines for more information.

## Citation

If you use this dataset in your research or application, please cite it as follows:

```
@dataset{lowerated_lm6_imdb_reviews_aspects_2024,
  author = {Muhammad Wisal},
  title = {IMDB Reviews (Aspect Based Formatted)},
  year = {2024},
  url = {https://github.com/Lowerated/lm6-movies-reviews-aspects}
}
```

## Acknowledgments

We extend our gratitude to the contributors and the Lowerated team for their efforts in creating and maintaining this dataset.